{
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  },
  "orig_nbformat": 4,
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.7.1 64-bit ('Python3_7_2')"
  },
  "interpreter": {
   "hash": "ceed3ede7d2ae4746b1bde0ed48f83d28ba93d0b68e140a25bb2fbb7cbabeb22"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "'\\n本例通过CNN方式对cifar-10数据集进行图像分类\\n'"
      ]
     },
     "metadata": {},
     "execution_count": 1
    }
   ],
   "source": [
    "\"\"\"\n",
    "本例通过CNN方式对cifar-10数据集进行图像分类\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import cv2\n",
    "import tensorflow as tf \n",
    "import matplotlib.pyplot as plt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#这是官方给的分类标签\n",
    "class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',\n",
    "               'dog', 'frog', 'horse', 'ship', 'truck']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "cifar_dir = r'F:\\AI\\Datasets\\dowload\\cifar-10\\cifar-10-batches-py'\n",
    "\n",
    "def get_cifar_paths(cf_dir):\n",
    "    cifar_paths = []\n",
    "    fs = os.listdir(cf_dir)\n",
    "    for i in fs:\n",
    "        if i.startswith('data_batch'):\n",
    "            cf_path = os.path.join(cf_dir,i)\n",
    "            cifar_paths.append(cf_path)\n",
    "    return cifar_paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "['F:\\\\AI\\\\Datasets\\\\dowload\\\\cifar-10\\\\cifar-10-batches-py\\\\data_batch_1',\n",
       " 'F:\\\\AI\\\\Datasets\\\\dowload\\\\cifar-10\\\\cifar-10-batches-py\\\\data_batch_2',\n",
       " 'F:\\\\AI\\\\Datasets\\\\dowload\\\\cifar-10\\\\cifar-10-batches-py\\\\data_batch_3',\n",
       " 'F:\\\\AI\\\\Datasets\\\\dowload\\\\cifar-10\\\\cifar-10-batches-py\\\\data_batch_4',\n",
       " 'F:\\\\AI\\\\Datasets\\\\dowload\\\\cifar-10\\\\cifar-10-batches-py\\\\data_batch_5']"
      ]
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "cifar_paths = get_cifar_paths(cifar_dir)\n",
    "cifar_paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "读取数据，该数据是二进制格式，通过字节的方式存储，需要通过pickle库来读取\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = None \n",
    "with open(cifar_paths[0],'rb') as r:\n",
    "    data = pickle.load(r,encoding='bytes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<class 'dict'> 4\n"
     ]
    }
   ],
   "source": [
    "print(type(data),len(data))  #每个data_batch返回的是一个dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "dict_keys([b'batch_label', b'labels', b'data', b'filenames'])"
      ]
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "data.keys()  # 该dict的键是字节类型，所以需要带b开头，也可以通过encode('utf8')来转换。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "21 116\n"
     ]
    }
   ],
   "source": [
    "print(len(data[b'batch_label']),data[b'batch_label'][0])  #每批次标签"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "10000 6\n"
     ]
    }
   ],
   "source": [
    "print(len(data[b'labels']),data[b'labels'][0])  #这是每张图片的标签 ，10000张图片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "10000 3072 [ 59  43  50 ... 140  84  72]\n"
     ]
    }
   ],
   "source": [
    "print(len(data[b'data']),len(data[b'data'][0]),data[b'data'][0])  #这是每张图片的numpy数据，10000张，并且都被打平了，需要reshape为(32,32,3)的格式即(height,width,channle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "(10000, 3072)\n"
     ]
    }
   ],
   "source": [
    "print(data[b'data'].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "10000 b'leptodactylus_pentadactylus_s_000004.png'\n"
     ]
    }
   ],
   "source": [
    "print(len(data[b'filenames']),data[b'filenames'][0])  #图片名称"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#定义一个处理函数来读取这些二进制数据\n",
    "def load_data(batch_path):\n",
    "    \"\"\"\n",
    "    batch_path : 二进制batch_data路径\n",
    "    \"\"\"\n",
    "    data = None \n",
    "    with open(batch_path,'rb') as r:\n",
    "        data = pickle.load(r,encoding='bytes')\n",
    "    \n",
    "    return data \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#将所有二进制图片与其对应的标签分别提取处理\n",
    "all_imgs = []\n",
    "all_labels = []\n",
    "for i in cifar_paths:\n",
    "    data = load_data(i)\n",
    "    all_imgs.extend(data[b'data'])\n",
    "    all_labels.extend(data[b'labels'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "50000 50000\n"
     ]
    }
   ],
   "source": [
    "print(len(all_imgs),len(all_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(32, 32, 3)"
      ]
     },
     "metadata": {},
     "execution_count": 16
    }
   ],
   "source": [
    "#直接reshape为32，32，3是不行的，因为最里面是r,b,g对应数据\n",
    "rimg = all_imgs[10].reshape(32,32,3)\n",
    "rimg.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x20a90577278>"
      ]
     },
     "metadata": {},
     "execution_count": 17
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<!-- Created with matplotlib (https://matplotlib.org/) -->\r\n<svg height=\"248.852656pt\" version=\"1.1\" viewBox=\"0 0 251.555625 248.852656\" width=\"251.555625pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n <defs>\r\n  <style type=\"text/css\">\r\n*{stroke-linecap:butt;stroke-linejoin:round;}\r\n  </style>\r\n </defs>\r\n <g id=\"figure_1\">\r\n  <g id=\"patch_1\">\r\n   <path d=\"M 0 248.852656 \r\nL 251.555625 248.852656 \r\nL 251.555625 0 \r\nL 0 0 \r\nz\r\n\" style=\"fill:none;\"/>\r\n  </g>\r\n  <g id=\"axes_1\">\r\n   <g id=\"patch_2\">\r\n    <path d=\"M 26.915625 225.041719 \r\nL 244.355625 225.041719 \r\nL 244.355625 7.601719 \r\nL 26.915625 7.601719 \r\nz\r\n\" style=\"fill:#ffffff;\"/>\r\n   </g>\r\n   <g 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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.imshow(rimg)  #乱位像素"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#图片数据转换为图片格式，方法一，填空法，先分成r,g,b数据再把r,g,b放入最高维。\n",
    "image=all_imgs[10]\n",
    "image=image.reshape(-1,1024)\n",
    "r=image[0,:].reshape(32,32)#红色分量\n",
    "g=image[1,:].reshape(32,32)#绿色分量\n",
    "b=image[2,:].reshape(32,32)#蓝色分量\n",
    "img=np.zeros((32,32,3))\n",
    "#RGB还原成彩色图像\n",
    "img[:,:,0]=r\n",
    "img[:,:,1]=g\n",
    "img[:,:,2]=b\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "metadata": {},
     "execution_count": 29
    }
   ],
   "source": [
    "img.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x20a946dad30>"
      ]
     },
     "metadata": {},
     "execution_count": 20
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.imshow(img/255.0)  #注意此时是float64类型，图片数值需要归一到0-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#方法二，先分r,g,b，再通过transpose，直接调换通道到最高维\n",
    "img2 = all_imgs[10]\n",
    "rimg2 = img2.reshape(3,32,32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "t_rimg2 = rimg2.transpose([1,2,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(32, 32, 3)"
      ]
     },
     "metadata": {},
     "execution_count": 23
    }
   ],
   "source": [
    "t_rimg2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "dtype('uint8')"
      ]
     },
     "metadata": {},
     "execution_count": 30
    }
   ],
   "source": [
    "t_rimg2.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x20a946536d8>"
      ]
     },
     "metadata": {},
     "execution_count": 24
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.imshow(t_rimg2)  #uinit8类型，则可以直接使用，不需要归一0-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#转换所有图片数据\n",
    "reshaped_imgs = []\n",
    "for img in all_imgs:\n",
    "    rgb_img = img.reshape(3,32,32)\n",
    "    t_img = rgb_img.transpose([1,2,0])\n",
    "    reshaped_imgs.append(t_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#创建数据集\n",
    "\n",
    "dataset = tf.data.Dataset.from_tensor_slices((reshaped_imgs,all_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "50000 <TensorSliceDataset shapes: ((32, 32, 3), ()), types: (tf.int32, tf.int32)>\n"
     ]
    }
   ],
   "source": [
    "print(len(dataset),dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "782 <BatchDataset shapes: ((None, 32, 32, 3), (None,)), types: (tf.int32, tf.int32)>\n"
     ]
    }
   ],
   "source": [
    "dataset = dataset.shuffle(len(dataset)).batch(64)\n",
    "print(len(dataset),dataset)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#创建CNN模型\n",
    "model = tf.keras.models.Sequential()\n",
    "model.add(tf.keras.layers.Conv2D(32,(3,3),activation='relu',input_shape=(32,32,3)))\n",
    "model.add(tf.keras.layers.MaxPool2D((2,2)))\n",
    "model.add(tf.keras.layers.Conv2D(64,(3,3),activation='relu'))\n",
    "model.add(tf.keras.layers.MaxPool2D((2,2)))\n",
    "model.add(tf.keras.layers.Conv2D(64,(3,3),activation='relu'))\n",
    "model.add(tf.keras.layers.Flatten())\n",
    "model.add(tf.keras.layers.Dense(64,activation='relu'))\n",
    "model.add(tf.keras.layers.Dense(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Model: \"sequential_1\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\nconv2d_3 (Conv2D)            (None, 30, 30, 32)        896       \n_________________________________________________________________\nmax_pooling2d_2 (MaxPooling2 (None, 15, 15, 32)        0         \n_________________________________________________________________\nconv2d_4 (Conv2D)            (None, 13, 13, 64)        18496     \n_________________________________________________________________\nmax_pooling2d_3 (MaxPooling2 (None, 6, 6, 64)          0         \n_________________________________________________________________\nconv2d_5 (Conv2D)            (None, 4, 4, 64)          36928     \n_________________________________________________________________\nflatten (Flatten)            (None, 1024)              0         \n_________________________________________________________________\ndense (Dense)                (None, 64)                65600     \n_________________________________________________________________\ndense_1 (Dense)              (None, 10)                650       \n=================================================================\nTotal params: 122,570\nTrainable params: 122,570\nNon-trainable params: 0\n_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#编译模型\n",
    "model.compile(optimizer='adam',\n",
    "            loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "            metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch 1/10\n",
      "782/782 [==============================] - 22s 28ms/step - loss: 2.1265 - accuracy: 0.3251\n",
      "Epoch 2/10\n",
      "782/782 [==============================] - 26s 34ms/step - loss: 1.4701 - accuracy: 0.4703\n",
      "Epoch 3/10\n",
      "782/782 [==============================] - 24s 31ms/step - loss: 1.2898 - accuracy: 0.5409\n",
      "Epoch 4/10\n",
      "782/782 [==============================] - 28s 36ms/step - loss: 1.1729 - accuracy: 0.5833\n",
      "Epoch 5/10\n",
      "782/782 [==============================] - 29s 36ms/step - loss: 1.0817 - accuracy: 0.6195\n",
      "Epoch 6/10\n",
      "782/782 [==============================] - 30s 38ms/step - loss: 0.9981 - accuracy: 0.6503\n",
      "Epoch 7/10\n",
      "782/782 [==============================] - 28s 36ms/step - loss: 0.9338 - accuracy: 0.6732\n",
      "Epoch 8/10\n",
      "782/782 [==============================] - 29s 37ms/step - loss: 0.8657 - accuracy: 0.6976\n",
      "Epoch 9/10\n",
      "782/782 [==============================] - 28s 36ms/step - loss: 0.8084 - accuracy: 0.7188\n",
      "Epoch 10/10\n",
      "782/782 [==============================] - 32s 41ms/step - loss: 0.7596 - accuracy: 0.7343\n"
     ]
    }
   ],
   "source": [
    "#训练模型\n",
    "history = model.fit(dataset,epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "#预测\n",
    "num = 2021\n",
    "test1 = reshaped_imgs[num]\n",
    "label1 = all_labels[num]\n",
    "print(label1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(32, 32, 3)"
      ]
     },
     "metadata": {},
     "execution_count": 46
    }
   ],
   "source": [
    "test1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "tensor_test1 = tf.constant([test1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[ 1.035227  , -0.69821745,  0.59304476,  0.42691815,  1.6300211 ,\n",
       "        -0.4787779 , -0.9469143 ,  2.773547  , -2.052634  , -0.20075932]],\n",
       "      dtype=float32)"
      ]
     },
     "metadata": {},
     "execution_count": 50
    }
   ],
   "source": [
    "pre = model.predict([tensor_test1])\n",
    "pre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x20b32a494a8>"
      ]
     },
     "metadata": {},
     "execution_count": 53
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.imshow(test1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "9\n"
     ]
    }
   ],
   "source": [
    "num2 = 24601\n",
    "test2 = reshaped_imgs[num2]\n",
    "label2 = all_labels[num2]\n",
    "print(label2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[ 1.5495415, 10.169988 ,  1.0782255,  4.450695 , -1.7077172,\n",
       "         4.647013 ,  1.9003288,  1.6278207,  3.6652038, 16.211206 ]],\n",
       "      dtype=float32)"
      ]
     },
     "metadata": {},
     "execution_count": 52
    }
   ],
   "source": [
    "tensor_test2 = tf.constant([test2])\n",
    "pre2 = model.predict([tensor_test2])\n",
    "pre2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x20b38c25320>"
      ]
     },
     "metadata": {},
     "execution_count": 54
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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ovT091La7n8suWxvpQpWTp47TPn07NlHblp08I+7UGZ71Fi57RbKeenu7aJ+6cwmtdukytZ0Z5/LVS2+eSba/+OrbtM+RUxep7fwFXiS0UU/LfABgxXR7scivc/Uml20vBxl25X5+Dp8+e4na3j2blg67S9yP5WSP6souRCYo2IXIBAW7EJmgYBciExTsQmSCgl2ITOio9FYoFFHtTsteN2/khR7v3LEj2b5z80bax3u5rTcoArl7925qM5INNV0q0z5927ZR2zsXuLw2XeDfw6U5LjXNTKYzx6bOcwntjddPUtvPXztGba8d5ZloZy6nfWzUuO9lIrECwK23cwmzp0T0NQBG11/jaYWVLh4Wh994g9reLnAJc2qSZ8Tt3XVzsv3yOM+Ua3rax0iQ05VdiExQsAuRCQp2ITJBwS5EJijYhciEjs7Gd/f24K577k3aJg+/Svvdd89Isr1v4xba59Q7POGiO1gbamxmltqGbrot2d7bxWeRLwTbOxnUYzt3gdu28BWZMH4xnUxy7hyfjX93mi8n1T/El3i6cxNPrmm10rPdlSIf+3qQ0DJb58snNepBIs98epv1Gt8ewBOsrMXHsR6USh+/mE4MAhZUqhTFQlCDjhZz5MkzurILkQkKdiEyQcEuRCYo2IXIBAW7EJmgYBciExaV3sysCuCvAezCQvbAAXf/qpkNADgIYDeAIwAecXdeaAtAqVzBlh07k7bxAq8L1xq8Idle28D71H+dXlIHAJ77fz+jtnN1Lnf88z/6F8n22+++m/sxx2un3bY9neADAM/+4AfU9vNfH6a2Xx0+mvYjkBvnA8moXuPSIZO1FraZlsOcLE8FALUGl8NagaQUSW9T0+kElGJQ87AYJDZtG+K1DXsqXIr85S9+TW0bN6eTwAYGuLRcKPDxoH2W+L4/d/dbAXwYwMNmdieArwF42t33AngawJevee9CiI6xaLC7+5y7H2q/ngXwFoBtAD4O4Hvtt30XwINr5aQQYuVc0z27mW0DcB+AlwAMuPs4ALj7ZQD8N4cQYt1ZcrCbWReA7wP4UjvIr74JrJB++81s1MxGJyf4krxCiLVlScFuZhUATwH4obsfaDePmVlf274RQHJyzt2fcPcRdx/p38Cr0Qgh1pZFg93MegA8A+AFd79yPfZnATzcfv1ZAD9effeEEKvFUrLe7gXwAICbzeyP221PA/gzAAfN7AsAjgH43KJbcsCJtFVyXhPsnbfeTLbfcMN22uelU+kldQDgJ0G22dwcl4bsb3+YbB9/9x3a59I0395MLTjmM7y+21mS2QYAL7/yq7ShyaW36BvfgiWIWsZ7lovpTK5SIBm1AnmwENTkqwbLeTHJsavM5bUP3nEHtXVVknerAIBWg0uApTIPtXIx7cvUBF9eq0FkSg+q0C0a7O7+PAAmIH5isf5CiOsDPUEnRCYo2IXIBAW7EJmgYBciExTsQmRCRwtOwh0FIrHVyrxo46vvvptsf+sCl9d+eYbLU3uCJZ42buLLDL3zVjrb7PgUL9i4ZfhGatsUZHJtmeZPG84H2WE91bRwUnAuyTAZBwAsyA4L3KfSmxnvNDXBx7EWLHk1H9jApL7Aj5033URtY4Hs2WhyKfWWW/g2q9W0nNcMtjc8PJxsr5R54VZd2YXIBAW7EJmgYBciExTsQmSCgl2ITFCwC5EJHZXe6vUaTpMMMe/irkyRtdSmLC3vAMB8UDTwo/fdR20bNmygtjEi9ZX7uFy3d98HqK0VSCvvnuNrg507d47amHTogbwWFY60Eh9jCzLRSqW0nNRo8oKehRnuR8t54U4Pss2Y9FYP1o47deoUtZ08eYLaurt4wckbd+3itpvSMtqWzbz40403pKW85174e9pHV3YhMkHBLkQmKNiFyAQFuxCZoGAXIhM6mwgDoIX0bOyuG/lSSD/72SvJ9vHLvEaXB4kO5QqfqW82+cxupTutChw/fZr2ufzss9Q2N8OXVro4xpN85oMllFos4cX5eBRKfBa5K5hhLgf12LpJQo4FdesGgiQktPgsfk9PN7WVqJoQ1Gpzfg7c+YHbqW3LFj57PhgsGzW0bVuyvbe3l/bp70urRpWgtp6u7EJkgoJdiExQsAuRCQp2ITJBwS5EJijYhciERaU3M6sC+GsAuwA0ARxw96+a2ecB/AWAs+23Trv7PYtsDEUiDYxP8iSIejMtG50LlnGqdHFZ6Je/4HW6tm7dSm1Hjx5Pts/XgxpogcTTFUiAkewyNDhAbd1Ehuru5ksk9QbLJ/X39VHb5k2bqW3TxnS//j6+r54ql9CqZS4BVkgNNwBoNtMJLxYU0CsEsm2UQFMkdfeAWMIsVdhxX3vdQA9qDS5VZ/9zdz9kZt0AXjSzH7TbD7r7v1viNoQQ68iiP+Pdfc7dD7VfzwJ4C0D6KQAhxHXLNd2zm9k2APcBeKnd9IiZvWlmh8xs36p7J4RYNZYc7GbWBeD7AL7k7uMAvgNgwN33APgmgAOk334zGzWz0elpXhdcCLG2LCnYzawC4CkAP3T3AwDg7vP+29mAJwHsSfV19yfcfcTdR6JJJyHE2rJosJtZD4BnALzg7o9f0X5/e8IOAD4NYHRtXBRCrAZLmY2/F8ADAG42sz9utz0NYALAt8xsDsApAH+y2IYKxQKVlPo38tpve973vmT74BCXyebnec2yi5d4RtnM3BS13X3XB5Pt2wK5bmhwkNr6N/Asr2ogHZa7uMTDsp5KZf69XgkkwHKRnyLFoAZdkdR+Kwe1AUuBdFUI9hXVwms00nX+IgltZpKfA1NT3DYf1PKLJDE2JqUyH/tNg9uT7dGSUYsGu7s/D4CJhI+TdiHEdYaeoBMiExTsQmSCgl2ITFCwC5EJCnYhMqGjBScrpRJ2bEvLVPd8kD9tyySeeiAzeJAxFCQ1obubZydt6N+YbO/t4Q8LdQeZXOWg0GPLeYHFWp0XqqyTYpTufKwiLJCMmKwFAE4KRLYawXEFRTFhvF8z2KaRJcIKwdJh5S7+mQ0Mcpk1ylJrBQUznZzHzaBPuZyWZi04uXVlFyITFOxCZIKCXYhMULALkQkKdiEyQcEuRCZ0VHqzQgFVsj7Y5k1pWQsAerrTUki0LlskrxWDTK5CgUsybKNRptHUNM+SKji3tVqBVBbIaPVGekwKYUYZt3kgAUY4kdGirZWCIpDF6HMpRTJr+noWSVTlYF+VoHBntM1IemM06vz8LlfS6w5G2YG6sguRCQp2ITJBwS5EJijYhcgEBbsQmaBgFyITOiq9wZ1KEIFSRgv5zc7N0D5hMcRAeqtUgjW5Sul+hRKXaiIJLZK1ogKF0TplLBuq0JWWagB+XAt+UBOKxWu/VjSb/Jgj2SiSripRstwythet9RYMR2iNPk9GMTivSsSmrDchhIJdiFxQsAuRCQp2ITJBwS5EJixpNt7Mvg3go+0/XwHwrwFUARwEsBvAEQCPuPulaDvNVguTZGmd8csTtF9PlTz0H9QRQ1DPLJgQRiFYnqhIkniiRJIqWY4JAIqR/8EMcyOY2KUz2mQ5JmCRZJdgX6FiQGaFgwl3WrcOADzo2AwUD1ZDrxUdVzD4HnWMPrQokYfQCk7UElONVmE2/gCAve3lmecB/EsAXwPwtLvvxcLab19e4raEEOvAkoLd3X/k7m5mvQCGALwG4OMAvtd+y3cBPLg2LgohVoMl37Ob2b8FcAbALwD8XwAD7j4OAO5+GcCWNfFQCLEqLDnY3f0vAWwGsBXAv8Hv3s0lb2jNbL+ZjZrZ6OTE5LIdFUKsjGuajXf3BoAfARgBMGZmfQBgZhsBJCfn3P0Jdx9x95H+Df0r9VcIsUwWDXYz22xmf9h+XQbwEIBRAM8CeLj9ts8C+PFaOSmEWDlLkd4MwBfN7JsA6gCeAfAtAP8LwEEz+wKAYwA+t9iGqtUq9tz+gaStUuYJKKxyWcGCBI5ATWoExnI1SIQhddxsmUk3y6WwjKSKSCZj9eLa1mVY4gQaCqkXBwBRzo0XokSetCPRVS4eq2uXGxeD9YrOq4Gt25LtpUA6XvRMbGvnH0uYzgP4xGL9hRDXB3qCTohMULALkQkKdiEyQcEuRCYo2IXIBFtObaxl78zsPIDj7T8HAVzo2M458uO9yI/38g/Nj5vdfShl6Giwv2fHZqPuPrIuO5cf8iNDP/QzXohMULALkQnrGexPrOO+r0R+vBf58V5+b/xYt3t2IURn0c94ITJhXYLdzB40s1fN7LCZfXE9fGj78byZHTOz19v//mMH9323mb1yxd8DZvY3ZvZG+/+OVP5J+PF5Mxu7Ykx+2gEfqmb2IzN7u338X2y3d3RMAj/WY0y+bWZvtv89ZWa9Kx4Pd+/oPwC9WNDat2Mh6+4FAHd32o+2L88DGFmH/X4dwEUAr17R9pcA/rT9+k8BfGOd/Pg8gP/W4fGoAvjD9utuLJQ+u7PTYxL4sR5j8k/x29vs/9n2YUXjsR5X9nsBvOzuZ3yh8s2TyKxYpbs/CuCeq5o7XsCT+NFx3H3O3Q+1X88CeAvANnR4TAI/Oo6vQZHX9Qj2YQDnrvj7PBau8uuBA3iyfTvxDbOgGsbacz0V8Hyk/fPxkJnt6+SOzWwbgPsAvIR1HJOr/ADWYUxWu8jrek3QXV0qJr36wtrzSXe/BcBdAHYA2L9OfgBLLODZAb6DhZNqD4BvYmHNgI5gZl0Avg/gS+2Tel3GJOHHuoyJL7PIK2M9gv0MFp7z/Q1D7baO4+5z7f9nsFBu6/3r4UebJRXwXGvcfd7bN4VYuMXa04n9mlkFwFMAfujuB9rNHR+TlB/rNSbtfV9zkVfGegT7SwA+YmZb2z+bP4N1KFbZnnl9oP26DOBTAF7stB9XcF0U8DSz+82su/3np7FQXHSt99mDhS/bF9z98StMHR0T5kenx2TNirx2cobxipnGPwLwKwBvAHhsnXzoBvC/ARwFcBjAfwFQ6NC+v4KFNfNm2x/iP8HCL5y/a4/J3wEYWic//gMWCoi+3j6ZdnfAjwewsKzY61f8e7zTYxL40dExwcK9+HPtfb4J4L9i4cK8ovHQE3RCZIKeoBMiExTsQmSCgl2ITFCwC5EJCnYhMkHBLkQmKNiFyAQFuxCZ8P8BT/gbQP0Bq3cAAAAASUVORK5CYII=\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.imshow(test2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}